I'm done with using local LLMs for coding

Posted by dtdisapointingresult@reddit | LocalLLaMA | View on Reddit | 426 comments

I think gave it a fair shot over the past few weeks, forcing myself to use local models for non-work tech asks. I use Claude Code at my job so that's what I'm comparing to.

I used Qwen 27B and Gemma 4 31B, these are considered the best local models under the multi-hundred LLMs. I also tried multiple agentic apps. My verdict is that the loss of productivity is not worth it the advantages.

I'll give a brief overview of my main issues.

Shitty decision-making and tool-calls

This is a big one. Claude seems to read my mind in most cases, but Qwen 27B makes me give it the Carlo Ancelotti eyebrow more often than not. The LLM just isn't proceeding how I would proceed.

I was mainly using local LLMs for OS/Docker tasks. Is this considered much harder than coding or something?

To give an example, tasks like "Here's a Github repo, I want you to Dockerize it." I'd expect any dummy to follow the README's instructions and execute them.

Issues like having a 'docker build' that takes longer than the default timeout, which sends them on unrelated follow-ups (as if the task failed), instead of checking if it's still running. I had Qwen try to repeat the installation commands on the host (also Ubuntu) to see what happens. It started assuming "it must have failed because of torchcodec" just like that, pulling this entirely out of its ass, instead of checking output.

I tried to meet the models half-way. Having this in AGENTS.md: "If you run a Docker build command, or any other command that you think will have a lot of debug output, then do the following: 1. run it in a subagent, so we don't pollute the main context, 2. pipe the output to a temporary file, so we can refer to it later using tail and grep." And yet twice in a row I came back to a broken session with 250k input tokens because the LLM is reading all the output of 'docker build' or 'docker compose up'.

I know there's huge AGENTS.md that treat the LLM like a programmable robot, giving it long elaborate protocols because they don't expect to have decent self-guidance, I didn't try those tbh. And tbh none of them go into details like not reading the output of 'docker build'. I stuck to the default prompts of the agentic apps I used, + a few guidelines in my AGENTS.md.

Performance

Not only are the LLMs slow, but no matter which app I'm using, the prompt cache frequently seems to break. Translation: long pauses where nothing seems to happen.

For Claude Code specifically, this is made worse by the fact that it doesn't print the LLM's output to the user. It's one of the reasons I often preferred Qwen Code. It's very frustrating when not only is the outcome looking bad, but I'm not getting rapid feedback.

I'm not learning anything

Other than changing the URL of the Chat Completions server, there's no difference between using a local LLM and a cloud one, just more grief.

There's definitely experienced to be gained learning how to prompt an LLM. But I think coding tasks are just too hard for the small ones, it's like playing a game on Hardcore. I'm looking for a sweetspot in learning curve and this is just not worth it.

What now

For my coding and OS stuff, I'm gonna put some money on OpenRouter and exclusively use big boys like Kimi. If one model pisses me off, move on to the next one. If I find a favorite, I'll sign up to its yearly plan to save money.

I'll still use small local models for automation, basic research, and language tasks. I've had fun writing basic automation skills/bots that run stuff on my PC, and these will always be useful.

I also love using local LLMs for writing or text games. Speed isn't an issue there, the prompt cache's always being hit. Technically you could also use a cloud model for this too, but you'd be paying out the ass because after a while each new turn is sending like 100k tokens.

Thanks for reading my blog.